Looking at the dataset Animals2, available in R, which contains average brain and body weights for 62 species of land mammals and three dinosaurs, we wanted to answer the question of how well the body weight predicts the brain weight of a land animal.

At the same time, we were interested to know how well this prediction works if we split our group into subgroups, for example if we have a group only with rodents, or one only with primates, or one without the dinosaurs. Also, are there outliers in the main group and in the subgroups? If yes, which animals are those outliers?

Animal data is coming from:

Weisberg, S. (1985) Applied Linear Regression. 2nd edition. Wiley, pp. 144–5.; P. J. Rousseeuw and A. M. Leroy (1987) Robust Regression and Outlier Detection. Wiley, p. 57.

# The ratios are calculated on the model of the no dino subgroup, 
#because that model gave the best fit.
plot_ly(data = anim, x = ~animal, y = ~ratio, color = ~animal, type = 'bar') %>%  
  layout(showlegend = FALSE)

#the exact library() calls can be found in the server.R file 
#preparing the data:
Animals2 <- local({D <- rbind(Animals, mammals); unique(D[order(D$body,D$brain),])})
dinos<-c("Dipliodocus", "Triceratops", "Brachiosaurus")
rodents<-c("Mouse", "Rat", "Chinchilla", "Ground squirrel", "Golden hamster", 
           "Guinea pig", "Arctic ground squirrel", "African giant pouched rat", "Mountain beaver", "Yellow-bellied marmot")
primates<-c("Human", "Gorilla", "Chimpanzee", "Baboon", "Verbet", "Slow loris", 
            "Galago", "Owl monkey", "Rhesus monkey", "Potar monkey")
Animals3<-Animals2[!(row.names(Animals2) %in% dinos),]
Animals4<-Animals2[row.names(Animals2) %in% rodents,]
Animals5<-Animals2[row.names(Animals2) %in% primates,]
#fitting the model: -the dgroup is the data frame Animals2, Animals3, Animals4 
#or Animals5 according to the user selection
anim_fit<-lm(log(brain)~log(body), data=dgroup)
#for the r.squared (variation explained):
model<-summary(anim_fit)$r.squared
#for the outlier detection (Cook's distance):
cutoff <- 4/((nrow(dgroup)-length(anim_fit$coefficients)-2)) 
plot(anim_fit, which=4, cook.levels=cutoff)

In general, we see that humans have the biggest brain/body weight ratio, while dinosaurs have the biggest body/brain weight ratio. Within the primates subgroup, we see that the human is an outlier for having bigger brain/body weight ratio, while the gorilla has the smallest brain/body weight ratio.

It would be very interesting to include many other morphological measurements and many more animals so as to be able to train a machine learning algorithm in identifying different animals (or groups of animals) based on their measurements.

The models can be tried out in the app: https://konstantina.shinyapps.io/land_animals/

And the server.R and ui.R files on github: https://github.com/ConnieKyr/land_animals